Adaptive data-driven reduced-order modelling techniques for nuclear reactor analysis

Research output: ThesisDissertation (TU Delft)

8 Downloads (Pure)

Abstract

Large-scale complex systems require high-fidelity models to capture the dynamics of the system accurately. For example, models of nuclear reactors capture multiphysics interactions (e.g., radiation transport, thermodynamics, heat transfer, and fluid mechanics) occurring at various scales of time (prompt neutrons to burn-up calculations) and space (cell and core calculations). The complexity of thesemodels, however, renders their use intractable for applications relying on repeated evaluations, such as control, optimization, uncertainty quantification, and sensitivity studies.
Original languageEnglish
Awarding Institution
  • Delft University of Technology
Supervisors/Advisors
  • Kloosterman, J.L., Supervisor
  • Lathouwers, D., Supervisor
  • Perko, Z., Advisor
Award date6 Oct 2020
Print ISBNs978-94-6421-022-4
DOIs
Publication statusPublished - 2020

Keywords

  • Proper Orthogonal Decomposition
  • Locally adaptive sparse grids
  • Greedy
  • Nonintrusive
  • Machine learning
  • Uncertainty quantification
  • Sensitivity analysis
  • Molten Salt Reactor
  • Large-scale systems

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